Object-oriented Classification Method Research Articles

Classification methods for hyperspectral remote sensing images with weak texture features

In order to provide method support for remote sensing image classification of weak texture region and improve the classification effect, a remote sensing image classification model for weak texture region was constructed. Firstly, the Gray Level Co-occurrence Matrix (GLCM) was used to extract texture features, then the correlation between texture features was reduced by dimensionality reduction method, and finally the spectral features were fused to classify remote sensing images. In the experimental part, neural network classifier was used to classify images based on texture feature, spectral feature and spectral features combined with texture features, and the results ware compared with object-oriented classification method. The analysis of texture extraction in the study area shows that the texture feature image obtained in the 5 × 5 window has higher resolution than that in the 7 × 7 window, and more robust texture features can be obtained in the range of 4∼8 pixel pairs. When using neural network classifier for classification, compared with images based on spectral features and texture features, the overall classification accuracy (OA) of the proposed classification model is increased by 16.72% and 11.16%, respectively, and the Kappa coefficient is increased by 0.2824 and 0.1943, respectively. Compared with the object-oriented classification method, the overall classification accuracy of the proposed classification scheme is increased by 1.15%, and the Kappa coefficient is increased by 0.0191. The constructed model has good classification effect and high classification accuracy for remote sensing images of weak texture region, and can provide scientific reference for remote sensing image classification of weak texture region.

Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson–mRMR–Machine Learning Model

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources.

Object-Oriented Convolutional Neural Network for Forest Stand Classification Based on Multi-Source Data Collaboration

Accurate classification of forest stand is crucial for protection and management needs. However, forest stand classification remains a great challenge because of the high spectral and textural similarity of different tree species. Although existing studies have used multiple remote sensing data for forest identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic complex forest stand identification using deep learning methods still require further exploration. Therefore, this study proposed an object-oriented convolutional neural network (OCNN) classification method, leveraging data from Sentinel-2, RapidEye, and LiDAR to explore classification accuracy of using OCNN to identify complex forest stands. The two red edge bands of Sentinel-2 were fused with RapidEye, and canopy height information provided by LiDAR point cloud was added. The results showed that increasing the red edge bands and canopy height information were effective in improving forest stand classification accuracy, and OCNN performed better in feature extraction than traditional object-oriented classification methods, including SVM, DTC, MLC, and KNN. The evaluation indicators show that ResNet_18 convolutional neural network model in the OCNN performed the best, with a forest stand classification accuracy of up to 85.68%.

Dynamics of coastal land-based aquaculture pond in China and Southeast Asia from 1990 to 2020

The extensive expansion of coastal land-based aquaculture ponds (CLAP) has resulted in substantial wetland loss. To effectively monitor the dynamics of CLAP on a large scale, critical spatial information derived from continuous datasets is essential. Here, we generate the inaugural map depicting the changes in aquaculture pond coverage across the coastal regions both of China and Southeast Asia from 1990 to 2020 by utilizing the long time-series Landsat archive with an object-oriented hierarchical classification method. We extracted coastal water bodies using the Otsu algorithm and established a water inundation frequency index to eliminate interference from temporary water facilities. This process allowed us to identify potential distribution areas of aquaculture ponds. To precisely map the spatial distribution of CLAP within these identified areas, we employed an object-oriented K-nearest neighbor approach with spectral, geometric, and water index features as classification features. The cumulative coverage of CLAP in China and Southeast Asia reached 23,121 km2 by 2020, marking a net increase of 12,700 km2 since 1990. Notably, China, Indonesia, and Vietnam emerged as the primary contributors to these changes within our study area. Regions exhibiting dense aquaculture activities are predominantly concentrated in the northern region above 10 degrees latitude and along the eastern coastlines of various countries. This aquaculture pond map reserves fine spatial detail by effectively distinguishing drainage channels, rice seedling fields, and embankments surrounding aquaculture ponds. It serves as a valuable resource for guiding coastal resource management and environmental protection initiatives.

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios.

Susceptibility Mapping of Unhealthy Trees in Jiuzhaigou Valley Biosphere Reserve

Jiuzhaigou Valley is recognized as both a world natural heritage site and a biosphere reserve. Conducting research on vegetation health within its scope can provide a demonstration role for sustainable development research. In this study, we proposed a technology integration approach that combined remote sensing intelligent identification and quantitative retrieval, and achieved vegetation health monitoring and susceptibility mapping of unhealthy trees. Leveraging WorldView-2 high-resolution satellite images, unhealthy trees were elaborately identified through the object-oriented classification method employing spectral and texture features, with F1 Score exceeding 75%. By applying fuzzy operations on indices related to leaf pigment and canopy architecture, we ultimately generated susceptibility maps of unhealthy trees on Sentinel-2 satellite images, with Area Under the Curve (AUC) exceeding 0.85. Our findings underscore that the vegetation health in Jiuzhaigou Valley is predominantly influenced by human activities and geological hazards. The forests of Jiuzhaigou Valley exhibit a certain degree of resilience to geological disasters, while human activities have been continuously exerting adverse effects on forest health in recent years, necessitating heightened attention. The methodology proposed in this study for mapping unhealthy trees susceptibility presents a cost-effective solution that can be readily applied for vegetation health monitoring and early warning in analogous biosphere reserves.

Mapping 30-m cotton areas based on an automatic sample selection and machine learning method using Landsat and MODIS images

ABSTRACT Cotton is one of the most significant cash crops in the world, and it is also the main source of natural fiber for textiles. It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale. However, previous research studies have predominantly concentrated on specific years using remote sensing data. Challenges still exist in the extraction of cotton areas for long time series with high accuracy. To address this issue, a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale. Bortala and Shuanghe in Xinjiang, China, were selected as the study cases to demonstrate the approach. Specifically, the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands. Then, the cotton samples were selected using the Normalized Difference Vegetation Index (NDVI) series of Moderate Resolution Imaging Spectroradiometer (MODIS) based on its phenological characteristics. Next, cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model. Finally, the performance was evaluated, and the spatiotemporal distribution characteristics of cotton planting areas were analyzed. The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution. The performance evaluation indicated the applicability and suitability of the method, there is a good correlation between the extracted cotton areas and statistical data, and the cotton area of the study area showed an increasing trend. The cotton spatial distribution pattern developed from dispersion to agglomeration. The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.

Cost–Benefit Evaluation of Walnut and Jujube Orchards under Fruit Tree–Crop Intercropping Conditions in Southern Xinjiang

Fruit planting plays an essential role in achieving zero hunger, ensuring food security, and increasing the income of local people. As one of China’s principal fruit-planting areas, southern Xinjiang possesses more than 80% of the total fruit-planting areas of Xinjiang. However, the spatial distribution, extent, and types of fruit trees remain unknown, generating uncertainty in calculating the economic benefits of orchards in this area. In this paper, we used walnut and jujube orchards under fruit tree–crop intercropping conditions in the Hotan Oasis in southern Xinjiang as the research object. Walnut and jujube orchards were precisely characterized using object-oriented and decision tree classification methods. Then, the economic benefits for farmers were estimated based on field surveys and statistical data. From 2003 to 2020, the area of jujube orchards rapidly increased from 1.91 × 103 ha to 33.59 × 103 ha, while that of walnut orchards steadily increased from 18.05 × 103 ha to 40.24 × 103 ha. The expansion areas of walnut orchards were mainly transformed from other orchards in the oasis, while the expansion areas of jujube orchards mainly originated from newly cultivated land in the desert. The increases in areas and yields largely offset the increase in planting costs and the decrease in purchase prices, resulting in an increasing trend in the total net income of the orchards. From 2003 to 2020, the total net income of walnut orchards increased by 68.96%, and the total net income of jujube orchards significantly increased by 23.37 times. However, the output/input ratios of walnut orchards under intercropping conditions were approximately two times higher than those of jujube orchards under monocropping conditions. The increase in investment slightly offset the decreases in yields and purchase prices, resulting in increases of 5.24% and 18.64% in the output/input ratios of walnuts and jujubes, respectively, in orchards exhibiting standardized cultivation. In the future, it is necessary to significantly expand the area of orchards involving standardized cultivation and improve the yield and quality of fruits, thereby increasing the yields and income levels of local farmers.

Remote sensing methods for striped marine oil spill detection in narrow ship channels

Maritime transport is the backbone of international trade and the global economy. With the increasing frequency of maritime transport, oil spills are on the rise and so is the risk of oil spills in shipping lanes. Therefore, timely oil spill monitoring at the ship channel is of far-reaching significance for marine disaster prevention and control. This study selects oil spill in the ship channel of the northern Yellow Sea as an example and proposes an object-oriented fuzzy logic classification method combining selected multi-feature parameters based on Sentinel-2 data. Firstly, sensitive bands for oil spills are selected to perform multi-scale segmentation of the oil spill object. Then, 30 samples of oil spill objects were uniformly selected in the oil spill area and the average fuzzy membership values were calculated using 11 membership functions by combining the preferred spectral, geometric, texture, and custom features. Finally, five fuzzy logic operators are used to achieve the final classification, while the oil spill misclassification information is processed and compared with the pixel-based SVM and RF algorithms for accuracy. The experimental results show that the oil spill detection based on the “AND” operator is the best compared to other fuzzy operators, with an overall accuracy of 85.19% and a KAPPA coefficient of 0.753. Relative to the pixel-based Support Vector Machine (SVM) and Random Forest (RF) algorithms, the overall accuracy was improved by 15.66% and 11.25%, respectively, and the KAPPA coefficients were improved by 0.072 and 0.161, respectively. The method is feasible for oil spill detection at ship channels and provides technical support for future marine oil spill detection work.

Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system

Rapid damage assessment of regional buildings is critical in post-earthquake rescue and victim resettlement. In this study, a novel rapid damage assessment method for post-earthquake buildings based on multi-types of damage features was proposed. First, the roof-related damage features are extracted from UAV tilt photography with the method of multiscale segmentation and object-oriented classification. Then, the normalized digital surface model was introduced to determine the height-related damage features. Meanwhile, a lightweight CNN model was employed to realize the preliminary evaluation of building facade conditions. Finally, all obtained damage features were taken as inputs to an adaptive-network-based fuzzy inference system for damage assessment. The proposed method was characterized by the capability to comprehensively cover the damage features of regional buildings involved in the evaluation, and thus be able to obtain more accurate judgments than current methods based on a single feature. At the same time, the effectiveness and robustness of the proposed method were evaluated and demonstrated by the field test on the world's largest earthquake site in Beichuan, China, and the prediction of damage level on the testing set achieved an accuracy of 87.1%.

Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings.

Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region

A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km2), while the highest negative effects were observed in 2017 (54.54 km2). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures.

Urban Vulnerability Analysis Based on Micro-Geographic Unit with Multi-Source Data—Case Study in Urumqi, Xinjiang, China

This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi’s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities.

Monitoring Spartina alterniflora Expansion Mode and Dieback Using Multisource High-Resolution Imagery in Yancheng Coastal Wetland, China

Spartina alterniflora (smooth cordgrass), China’s most common invasive species, has posed significant challenges to native plant communities and coastal environments. Monitoring the invasion and dieback process of S. alterniflora by multisource high-resolution imagery is necessary to manage the invasion of the species. Current spatial analyses, however, are insufficient. As a result, we first extracted S. alterniflora by integrating multisource high-resolution images through the multiscale object-oriented classification method, then identified the expansion patterns of S. alterniflora on the seaward side by the landscape expansion index, and conformed the main drivers of S. alterniflora dieback on the landward side in the Jiangsu Dafeng Milu National Nature Reserve. The findings revealed that the area of S. alterniflora decreased in size from 1511.26 ha in 2010 to 910.25 ha in 2020. S. alterniflora continues to grow to the sea and along the tidal creek on the seaward side, with a total increase of 159.13 ha. External isolation expansion patterns accounted for 65.16% of the total expansion patches, with marginal expansion patches accounting for 24.22% and tidal creek-leading expansion patches accounting for 10.62%. While the landward side showed a declining trend, the total area decreased by 852.36 ha, with an annual average change rate of 8.67%. S. alterniflora dieback was negatively related to the number of tidal creeks and positively related to the number of wild Elaphures davidianus and the length of artificial ditches. Our findings provide a scientific foundation for the ecological control of S. alterniflora. Its presence in coastal wetlands inspires evidence-based protection and management strategies to protect the coastal wetland ecosystem.

Research on the identification of land types and tree species in the Engebei ecological demonstration area based on GF-1 remote sensing

Identifying forest types is crucial in satellite remote sensing monitoring. Research focusing on the identification of forest tree species using high-resolution remote sensing images is on the rise. Traditional pixel-based classification methods have not been successful in fully utilizing the rich spatial and texture feature information present in the image. Further, these methods are plagued by “classification noise”, which impedes the precise extraction of information regarding forest tree species. The object-oriented classification method, on the other hand, offers an extraction methodology rooted in image segmentation object features. It efficiently leverages the spectral, texture, and spatial geometry information of high-resolution remote sensing images. In this study, an initial application of the object-oriented, multi-level information extraction was conducted on ground objects within the Engebei ecological demonstration area. This was performed utilizing Gaofen (GF)-1 remote sensing images. Following this, a multi-scale segmentation algorithm was utilized to establish different segmentation scales relative to the features of the different objects. The optimal segmentation scale, shape factor, and compactness factor were determined to be 222/0.5/0.5, 167/0.5/0.6, and 81/0.4/0.6, respectively, for the three levels. Subsequently, training samples were used to select optimal features during the classification process. The nearest neighbor classification and random forest classification were performed respectively. The results indicate an overall classification accuracy of 88.58% and 87.26% for the nearest neighbor classification of remote sensing images in 2020 and 2021 respectively. Meanwhile, the overall classification accuracy for the random forest classification was 92.95% and 92.02% respectively. The Kappa coefficients for the nearest neighbor classification were 0.86 and 0.85, whereas those for the random forest classification were 0.92 and 0.90. These outcomes underscore the utility of the object-oriented classification method in enhancing the accuracy of forest type identification.

Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.

Spatiotemporal changes of wetlands in China during 2000–2015 using Landsat imagery

China has abundant wetland resources; however, wetland ecosystems are experiencing critical challenges owing to continuous anthropogenic activities and climate change. Therefore, rapid and accurate mapping of wetlands and analysis of the drivers of spatiotemporal changes in wetlands are of great importance for wetland resource conservation. In this study, we proposed a comprehensive method for wetland mapping in China based on Landsat images, in which inland wetlands were identified by the object-oriented and hierarchical classification method, and coastal wetlands were extracted by visual interpretation based on sea boundaries and sea level nodes. Additionally, partial least squares structural equation modelling was utilized to exploit the mechanisms of spatiotemporal variability in wetlands during 2000–2015. The application of the comprehensive method to Landsat TM/OLI resulted in wetland maps of China with overall classification accuracies above 85%. The national wetland maps demonstrated that the wetland areas of China in 2000 and 2015 were 476,305.68 and 456,262.01 km2, respectively, and swamps accounted for the largest proportion of the total wetland area in China (over 38%). Statistics show that wetlands are mainly located in the northern inland areas, such as Tibet, Inner Mongolia, and Xinjiang Autonomous Region, as well as the Qinghai and Heilongjiang Provinces. Wetland changes were concentrated in the Tibetan Plateau, Loess Plateau, Song-Liao Plain, and middle and lower reaches of the Yangtze River. Human activities and climate change were the primary contributors to wetland changes in China. These two factors can not only directly influence wetland changes but also can be indirectly influenced by other factors, such as soil and topography. The results of this study are intended to provide basic data for monitoring and conserving wetland resources in China.

A dataset of spatial distribution of highland barley planting area on the Qinghai-Tibet Plateau (2019)

Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.

Optimal Scale Selection and an Object-Oriented Method Used for Measuring and Monitoring the Extent of Land Desertification

Desertification has become a major problem in the field, affecting both the global ecological environment and economy. The effective monitoring of desertified land is an important prerequisite for land desertification protection and governance. With the aim of addressing the problems of spectral confusion as well as the salt and pepper phenomenon concerning the successful extraction of desertification information by utilizing the pixel-based method in the studies, Landsat remote sensing images obtained from the year 2001 to 2021 were selected in this study as the data source, and then, the object-oriented random forest classification method was improved by using different optimal segmentation scale selection techniques and combining multi-thematic index characteristics for measuring the extent of land desertification. Finally, the improved method was applied to study the dynamic changes in desertification in the Mu Us Sandy Land Ecological Function Reserve. The results show that the optimal scale determined by different optimal segmentation scale selection methods is not entirely consistent, and a minor scale should be selected as the optimal scale. Compared with the pixel-based classification method, the overall accuracy of object-oriented classification based on the optimal segmentation scale was improved by 8.06%, the Kappa coefficient increased by 0.1114, and the salt and pepper phenomenon was significantly reduced. From 2001 to 2021, the area of desertified land decreased by 587.12 km2 and the area of severely desertified land decreased by 4115.92 km2, indicating that the control effect was remarkable. This study can provide effective decision-making evidence and support for the successful governance of desertification.

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach.

Fine classification of urban nighttime lighting is a key prerequisite step for small-scale nighttime urban research. In order to fill the gap of high-resolution urban nighttime light image classification and recognition research, this paper is based on a small rotary-wing UAV platform, taking the nighttime static monocular tilted light images of communities near Meixi Lake in Changsha City as research data. Using an object-oriented classification method to fully extract the spectral, textural and geometric features of urban nighttime lights, we build four types of classification models based on random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT), respectively, to finely extract five types of nighttime lights: window light, neon light, road reflective light, building reflective light and background. The main conclusions are as follows: (i) The equal division of the image into three regions according to the visual direction can alleviate the variable scale problem of monocular tilted images, and the multiresolution segmentation results combined with Canny edge detection are more suitable for urban nighttime lighting images; (ii) RF has the highest classification accuracy among the four classification algorithms, with an overall classification accuracy of 95.36% and a kappa coefficient of 0.9381 in the far view region, followed by SVM, KNN and DT as the worst; (iii) Among the fine classification results of urban light types, window light and background have the highest classification accuracy, with both UA and PA above 93% in the RF classification model, while road reflective light has the lowest accuracy; (iv) Among the selected classification features, the spectral features have the highest contribution rates, which are above 59% in all three regions, followed by the textural features and the geometric features with the smallest contribution rates. This paper demonstrates the feasibility of nighttime UAV static monocular tilt image data for fine classification of urban light types based on an object-oriented classification approach, provides data and technical support for small-scale urban nighttime research such as community building identification and nighttime human activity perception.